The Machine Behind the Money
How modern AI actually works
A plain-language account of what a large language model actually is, written for a numerate professional who has never seen a line of code and must never be assumed to have. It explains tokens, training and prediction; why generation is not retrieval; why hallucination is a structural property rather than a bug; and it draws the distinctions the rest of the course depends on — between narrow, generative, multimodal and agentic AI. By the end you can judge, from the mechanism rather than the marketing, where a tool can and cannot be trusted with money and personal data.
- Explain in plain terms how a generative model produces text or an image, and why the same prompt can yield different and sometimes fabricated output.
- Distinguish narrow, generative, multimodal and agentic AI, and say which financial tasks each is suited to and unsuited to.
- Judge from the mechanism, not from marketing, where a tool can and cannot be trusted with money and personal data.
- Reading a model's claims critically
- Separating retrieval from generation
- Recognising hallucination and over-confidence
- Matching tool class to financial task
Lessons in this module
How it lands across the four desks
You learn why a synthetic document or a fabricated narrative can look flawless: the machine that produced it was built to generate plausibility, not truth. That mental model is the foundation of every detection skill in Modules 2 to 4.
You learn why a confident, fluent answer from an AI assistant can still be wrong — and measurably often is on legal questions — and why verification against primary sources is a control, not a courtesy.
You learn why a client-ready draft still needs checking, what the model does and does not know about your client, and how to use generative tools for speed without inheriting their fabrications.
You learn why a score is a prediction, not a fact: the same statistical machinery that powers a chatbot's next word powers the probability behind a credit decision, with the same structural limits.
Key literature · 6 sources
Every module rests on a verified scholarly and institutional evidence base. The full core and further reading lists open with the module.
- Vaswani, A. et al. (2017) 'Attention Is All You Need.' NeurIPS 2017. arxiv.org/abs/1706.03762 — the transformer architecture underlying modern language models.
- Shanahan, M. (2024) 'Talking About Large Language Models.' Communications of the ACM 67(2) — the discipline of describing what models actually do.
- Ji, Z. et al. (2023) 'Survey of Hallucination in Natural Language Generation.' ACM Computing Surveys 55(12) — why fabrication is structural.
- Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. (2021) 'On the Dangers of Stochastic Parrots.' FAccT '21 — pattern fluency without truth-tracking.
- Bommasani, R. et al. (2021) 'On the Opportunities and Risks of Foundation Models.' Stanford CRFM — why one model's failure modes propagate everywhere.
- SARB Prudential Authority & FSCA (2025) 'Artificial Intelligence in the South African Financial Sector' — the local adoption, risk and skills picture; the sector-survey findings cited throughout this module.